Novel Ensemble Learning Approach for Predicting COD and TN: Model Development and Implementation

被引:1
|
作者
Cheng, Qiangqiang [1 ]
Kim, Ji-Yeon [2 ]
Wang, Yu [1 ]
Ren, Xianghao [1 ]
Guo, Yingjie [1 ]
Park, Jeong-Hyun [3 ]
Park, Sung-Gwan [2 ]
Lee, Sang-Youp [2 ]
Zheng, Guili [4 ]
Wang, Yawei [4 ]
Lee, Young-Jae [5 ]
Hwang, Moon-Hyun [2 ]
机构
[1] Beijing Univ Civil Engn & Architecture, Key Lab Urban Stormwater Syst & Water Environm, Minist Educ, Beijing 100044, Peoples R China
[2] Korea Univ, Inst Convers Sci, 145,Anam Ro, Seoul 02841, South Korea
[3] Seoul Natl Univ, Grad Sch Engn Practice, 1,Gwanak Ro, Seoul 08826, South Korea
[4] Xinhua Pharmaceut Shouguang Co Ltd, Res Ctr, 10 Chayan Rd, Shouguang 262700, Peoples R China
[5] Sungkyunkwan Univ, Grad Sch Water Resources, Dept Water Resources, Suwon 16419, South Korea
关键词
ensemble model; water quality prediction; COD & TN; A2O process; WWTPs; WATER TREATMENT-PLANT; WASTE-WATER; REMOVAL; DENITRIFICATION; EFFLUENT; STRENGTH;
D O I
10.3390/w16111561
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Wastewater treatment plants (WWTPs) generate useful data, but effectively utilizing these data remains a challenge. This study developed novel ensemble tree-based models to enhance real-time predictions of chemical oxygen demand (COD) and total nitrogen (TN) concentrations, which are difficult to monitor directly. The effectiveness of these models, particularly the Voting Regressor, was demonstrated by achieving excellent predictive performance even with the small, volatile, and interconnected datasets typical of WWTP scenarios. By utilizing real-time sensor data from the anaerobic-anoxic-oxic (A2O) process, the model successfully predicted COD concentrations with an R-2 of 0.7722 and TN concentrations with an R-2 of 0.9282. In addition, a novel approach was proposed to assess A2O process performance by analyzing the correlation between the predicted C/N ratio and the removal efficiencies of COD and TN. During a one and a half year monitoring period, the predicted C/N ratio accurately reflected changes in COD and TN removal efficiencies across the different A2O bioreactors. The results provide real-time COD and TN predictions and a method for assessing A2O process performance based on the C/N ratio, which can significantly aid in the operation and maintenance of biological wastewater treatment processes.
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页数:28
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